Regression models the relationship between a dependent variable and one or more independent variables. There are many different types of Regression models. Displayr offers its users to select between seven types of Regression models. Additionally, any of these models can be run in a stepwise mode.
The type of regression depends on the type and number of outcomes the model is aiming to predict.
- An Outcome variable. When using stacked data the Outcome variable should be a single question in a Multi type structure.
- Predictors variables will be considered as predictors of the outcome variable. When using stacked data the Predictor(s) need to be a single question in a Grid type structure.
- The type of Regression type required will depend on the outcome variable used in your Regression model. To find the Outcome variable select the output and go Inputs > Linear Regression > Outcome.
- OPTIONAL: To view all potential outcomes of the dependent variable go to the Data Sets tree, select the Outcome variable and go to the object inspector > Properties > DATA VALUES > Values. Alternatively, you can also drag and drop the variable onto the Page to create a table containing all outcomes.
- Go to Inputs > Regression Type and select the appropriate model depending on the number of categories of the Outcome variable.
Outcome variable Example Regression Type Two categories
1. Yes / Selected
2. No / Not selected
Three to 11 ordered
Ordered logit Three to 11 unordered
Multinomial Logit Regression 12 or more ordered
How would you rate your
happiness on a scale of 0 to
Linear regression Net Promoter Score
Linear regression Purchase or usage
Number of cans of coke
consumed per week
NBD (or, if you get a weird
Count data with the assumption that the dependent variable has a Poisson distribution
The number of births per hour during a given day
Poisson Regression Count data with overdispersed distribution Overdispersion occurs when the observed variance is higher than the variance of a theoretical model Quasi-Poisson